Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

HỢP CHẤT STEROID VÀ FLAVONE TỪ THÂN RỄ THIÊN NIÊN KIỆN LÁ LỚN (Homalomena pendula)

Phytochemical investigation of the rhizomes of Homalomena pendula resulted in the isolation of one flavone and three steroids. These compounds were determined as tangeretin (1), ergosterol peroxide (2), sitoindoside I (3), and stigmasterol (4) on the basis of 1D and 2D NMR data and in comparison with the available data in the literature. Compounds 1‒3 were found for the first time from the genus Homamomena. The n-hexane and ethyl acetate extracts show NO production inhibitory activity in RAW 264.7 macrophage cells with IC50 values of 46.8 and 75.52 µg·mL–1.Hợp chất flavone, tangeretin (1), và ba hợp chất steroid: ergosterol peroxide (2), sitoindoside I (3) và stigmasterol (4) đã được phân lập từ thân rễ của cây thiên niên kiện lá lớn (Homalomena pendula). Cấu trúc hóa học của chúng được xác định dựa trên phân tích dữ liệu phổ cộng hưởng từ hạt nhân (1D và 2D NMR) và so sánh với các tài liệu đã công bố. Các hợp chất (1-3) được phân lập lần đầu tiên từ chi Homalomena. Cao chiết n-hexane và ethyl acetate của cây này có hoạt tính ức chế sản sinh NO trên đại thực bào RAW 264.7 kích thích bằng lipopolysaccharide với các giá trị IC50 là 46,80 và 75,52 µg·mL–1

Terpenoids from Dacrycarpus imbricatus.

The phytochemical investigation of the hexane extract from the twigs and leaves of Dacrycarpus imbricatus (Blume) de Laub led to the isolation of a rare sesquiterpene, spathulenol (1) along with three diterpenes named pimaric acid (2), trans-communic acid (3) and cis-communic acid (4). Their structures were determined by combination of spectral analysis and comparison with reported data. This is the first report on isolation of compound 1 from the Podocarpaceae family. Keywords. Dacrycarpus imbricatus, spathulenol, pimaric acid, communic acid

Experience in Using Mobile Laboratory for Monitoring and Diagnostics in the Socialist Republic of Vietnam

The aim was to present the experience of using mobile laboratory for monitoring and diagnostics (MLMD) during the epizootiological monitoring of the northern provinces of Vietnam. MLMD was transferred by Federal Service for Surveillance in the Sphere of Consumers Rights Protection and Human Welfare to the Socialist Republic of Vietnam as part of implementation of cooperation programs on combating infectious diseases. The use of MLMD made it possible to obtain new information on the circulation of pathogens of natural-focal infectious diseases on the territory of Vietnam. It also provided the necessary conditions for conducting research using methods of express diagnostics, bacteriological analysis, performing a full cycle of work – from the receipt of samples to the disinfection and destruction of infected material in compliance with the requirements of biological safety in the field. The effectiveness of using mobile laboratories in response to the emergencies of sanitary and epidemiological nature, both to strengthen stationary laboratory bases and to organize diagnostic studies in remote regions, has been shown. The use of MLMD for the diagnosis of COVID‑19 has been an effective component of countering the new coronavirus infection in Vietnam and significantly increased the volume of testing in the country

A matrix approach to tropical marine ecosystem service assessments in South east Asia

Ecosystem service assessments are increasingly used to support natural resource management, but there is a bias in their application towards terrestrial systems and higher income countries. Tropical marine applications are particularly scarce, especially in SE Asia. Given the growing coastal population and expansion in blue economy sectors in SE Asia, evidence to support effective marine planning, such as ecosystem service assessments, is urgently needed. Data deficiencies for marine systems, especially (but not only) in lower income countries is a significant obstacle for ecosystem service assessments. To overcome this, we develop an ecosystem service potential matrix which combines evidence taken from an extensive literature review together with expert opinion. The matrix includes both natural and modified habitats as the service providing units. The ecosystem service potential for habitats are scored at the macro level (e.g. mangrove) due to insufficient evidence to score micro-habitats (e.g. fringe, basin or riverine mangroves). The majority of evidence is available for biogenic habitats (mangroves, coral reefs and seagrass meadows) with comparatively little for sedimentary habitats. While provisioning, regulating and cultural services are scored, published evidence is more readily available for provisioning and regulating services. Confidence scores, indicating the uncertainty in the ecosystem service potential scores are included in the matrix. To our knowledge this is the first attempt to systematically capture the provision of ecosystem services from tropical marine habitats. Although initially developed for four marine biosphere reserves and protected areas in SE Asia, the generic nature of the evidence included suggests that the matrix constitutes a valuable baseline for marine ecosystem service assessments within SE Asia and provides a robust foundation for development in future work

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Abstract Background MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of “disease modules” in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of “disease modules” in these networks

Numerical investigation of novel prefabricated hollow concrete blocks for stepped-type seawall structures

In this paper, a novel Three-Dimensional (3D) prefabricated Hollow Concrete Block (HCB), which can form seawall structures, is numerically investigated. Unlike the traditional gravity-type seawalls, this is a lightweight structure in comparison with the conventional ones, but it possesses advantageous features under complex loading conditions. The load bearing capacity of HCB itself under the wave loading has been examined. The highly complicated soil-structure interaction is taken into account so that the underlying simulation may predict realistic behaviors of seawall structures. The results indicate that this novel seawall model requires fewer concrete materials, but is adequate under sea wave attacks. For instance, stresses induced on seawall blocks are smaller than the allowed stresses of concrete. By adding sand into the HCB components to increase its weight and using supported-piles system, the stability of the seawall structure is enhanced and its settlement is reduced. The results obtained are evaluated using reference studies, from literature, regarding the elastic settlement of foundation. The techniques proposed in this paper, therefore, may provide an important pathway for further studies aiming to achieve optimal and reliable coastal designs taking into account the impacts of climate change